The exemplary embodiment relates generally to systems and methods for detecting image quality defects generated by an imaging device. It finds particular application in the diagnosis of print quality defects in printing systems using original customer documents.
Image non-uniformity occurs in the output images of digital image rendering devices, such as copiers, scanners, and printers, for a variety of reasons. Even relatively small non-uniformities can give rise to visibly objectionable print defects. In printing systems, physical alignments, component tolerances, wear and component age can influence the uniformity with which colorants, such as inks and toners, are laid down across the surface of print media. Streaks, for example, are one-dimensional image defects that generally run parallel to the process direction in the printed image. They can arise from non-uniform responses of the subsystems of an image rendering device, such as a xerographic marking engine, and can be constant over time in that they appear in relatively the same location from print to print. Photoreceptor scratches, contamination of the charging wire(s), non-uniform LED imager output and Raster Output Scanner (ROS) spot size variations, and spatially varying pressure on a bias transfer roll are examples of subsystem defects that can give rise to rendered image streaking in a xerographic marking engine. Bands are also one-dimensional image defects that generally run perpendicular to the process direction in a printed image. They are typically caused by time-varying performance of a marking engine subsystem, such as non-uniform velocity of the photoreceptor drive, out-of-roundness of development rolls, and wobble of the ROS polygon mirror. In a uniform patch of gray, streaks and bands may appear as a variation in the gray level. In general, “gray” refers to the optical density or area coverage value of any single color separation layer, whether the colorant is black, cyan, magenta, yellow, or some other color.
Detection of image quality defects ensures the production of quality images by image rendering devices. There are various approaches for detecting such defects. For example, test images designed to highlight any problems are rendered by an image rendering device, such as a printer, and captured by an image capturing device, such as a camera, a scanner, or the like. The captured images are compared with a standard test page designed to provide information about the performance of the printer. The existence of an artifact or defect in the image can thereby be detected. The customer or service representative uses the documents to diagnose printer problems, but generally only when the customer is already aware of a problem.
Although some image quality defects may not be visible, these defects may indicate that the device is deteriorating, and presage visible image quality defects. Other image quality defects may be largely of a geometric nature and caused by distortion due to slight warping of the rendered image, small misalignment of the captured image, mapping errors between the image capture device and the image rendering device, and other systematic defects of the image rendering devices not related to degradation of the rendering device. Therefore, another approach is to detect the image quality defects early, so the image rendering device may be adjusted or repaired to reduce or even avoid image quality defects. In this second approach, a full difference image between an ideal and a scanned customer document is computed. The difference image is then analyzed for defects.
Various methods exist for correcting image quality defects, once they are detected. These include modification of the tone reproduction curves (TRCs) used in converting the original image data into machine-dependent image data. One-dimensional TRCs are widely used in digital imaging as a means for compensating for non-linearities introduced by an individual imaging device. Other methods include replacing or repairing components of the image rendering system responsible for the image quality defects.